教授
数学学院
系统科学系
电话:
邮箱:
qsliu@seu.edu.cn
地址:
九龙湖图书馆547
邮编:
211189
基本信息
研究成果
项目与荣誉
社会兼职
个人简介
工作经历
教育经历
研究方向
神经网络;计算智能;分布式优化;多智能体系统;复杂网络;机器学习
Q. Liu,X. Le, and K. Li,“A distributed optimization algorithm based on multiagentnetwork for economic dispatch with region partitioning,”IEEE Transactions onCybernetics, 2019, in press.
K. Li, Q. Liu, S. Yang, J. Cao, and G. Lu, “Cooperative optimization of dualmultiagent system for optimal resource allocation,” IEEE Transactions on Systems,Man, and Cybernetics: Systems, 2019, in press.
S. Yang, J. Wang, and Q. Liu, “Cooperative-competitive multiagent systems fordistributed minimax optimization subject to bounded constraints,” IEEE Transactions on Automatic Control, vol. 64, no. 4, pp. 1358–1372, Apr. 2019.
B. Xu, Q. Liu, and T. Huang, “A discrete-time projection neural network for sparsesignal reconstruction with application to face recognition,” IEEE Transactions onNeural Networks and Learning Systems, vol. 30, no. 1, pp. 151–162, Jan. 2019.
Q. Liu, S. Yang, and Y. Hong, “Constrained consensus algorithms with fixed step size for distributed convex optimization over multiagent networks,” IEEE Transactions on Automatic Control, vol. 62, no. 8, pp. 4259–4265, Aug. 2017.
Q. Liu, S. Yang, and J. Wang, “A collective neurodynamic approach to distributed constrained optimization,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 8, pp. 1747–1758, Aug. 2017.
Q. Liuand J. Wang, “L1-minimization algorithms for sparse signal reconstruction based on a projection neural network,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 3, pp. 698–707, Mar. 2016.
Q. Liuand J. Wang, “A second-order multi-agent network for bound-constrained distributed optimization,” IEEE Transactions on Automatic Control, vol. 60, no. 12, pp. 3310–3315, Dec. 2015.
Q. Liuand J. Wang, “A projection neural network for constrained quadratic minimax optimization,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 11, pp. 2891–2900, Nov. 2015.
Q. Liu, T. Huang, and J. Wang, “One-layer continuous- and discrete-time projection neural networks for solving variational inequalities and related optimization problems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 7, pp. 1308–1318, July 2014.
Q. Liuand J. Wang, “A one-layer projection neural network for nonsmooth optimization subject to linear equalities and bound constraints,” IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 5, pp. 812–824, May 2013.
Q. Liu, C. Dang, and T. Huang, “A one-layer recurrent neural network for real-time portfolio optimization with probability criterion,” IEEE Transactions on Cybernetics, vol. 43, no. 1, pp. 14–23, Feb. 2013.
奖励和荣誉
2019年,吴文俊人工智能科学技术奖自然科学奖二等奖(排名第二),获奖项目:脑启发的计算模型理论与方法
2018年,江苏省第五期“333高层次人才培养工程”中青年学术技术带头人
2015年,湖北省自然科学奖二等奖(排名第一),获奖项目:微分系统的动态演化分析与优化模型设计
2015年,IEEE控制论汇刊杰出审稿人
2012年,入选教育部“新世纪优秀人才支持计划”
2012年,亚太神经网络联合会青年研究者奖(独立)
2011年,教育部高等学校自然科学奖一等奖(排名第二),获奖项目:神经动力学优化模型及应用
2011年,IEEE计算智能学会IEEE Transactions on Neural Networks杰出论文奖(排名第一),论文题目: A one-layer recurrent neural network with a discontinuous hard-limiting activation function for quadratic programming,第一作者
2009年,科学新闻与爱思唯尔(Elsevier)科技部主办的“SCOPUS寻找未来科学之星”信息领域“青年科学之星”
2006年,江苏省优秀硕士学位论文,论文题目:求解优化问题的神经网络方法
科研项目
军委科技委国防科技创新特区项目,2019年6月-2022年6月,主持
国家自然科学基金面上项目,**,基于模型分解和群集神经动力学网络的分布式优化算法研究,2019年1月-2022年12月,主持
国家自然科学基金重点项目,**,基于群体智能的分布式优化理论、方法及应用研究,2019年1月-2023年12月,参与
国家自然科学基金面上项目,**,基于神经网络和群体智能的稀疏表示算法研究,2015/01-2018/12,主持
教育部“新世纪优秀人才支持计划”,NCET-12-0114,计算智能,2013/01-2015/12,主持
国家自然科学基金青年科学基金项目,**,基于有限时间收敛的优化反馈神经网络设计、分析与应用,2012/01-2014/12,主持
江苏省自然科学基金面上项目,BK**,优化神经网络建模与有限时间收敛性分析,2012/01-2014/12,主持
教育部博士点基金,026,基于多目标规划条件的耦合优化神经网络设计,2010/01-2012/12,主持
期刊编委
2018-,IEEE Transactions on Neural Networks and Learning Systems
2015-,IEEE Transactions on Cybernetics
2012-,Neural Networks
2020-,Neural Processing Letters
期刊客座编委
Neurocomputing (2015-2016);Cognitive Computation (BICS 2012);Mathematics and Computers in Simulation (ISNN 2010);Computational and Mathematical Methods in Medicine (Computational Neuroscience2013)
For more information, please visit www.aipi.group.